OpenRaiser/Intern-Atlas huggingface dataset:
Evaluation of Trace Alignment Quality and its Application in Medical Process Mining
Venue: arXiv cs.OH 2017
Year: 2017

Trace alignment algorithms have been used in process mining for discovering the consensus treatment procedures and process deviations. Different alignment algorithms, however, may produce very different results. No widely-adopted method exists for evaluating the results of trace alignment. Existing reference-free evaluation methods cannot adequately and comprehensively assess the alignment quality. We analyzed and compared the existing evaluation methods, identifying their limitations, and introduced improvements in two reference-free evaluation methods. Our approach assesses the alignment result globally instead of locally, and therefore helps the algorithm to optimize overall alignment quality. We also introduced a novel metric to measure the alignment complexity, which can be used as a constraint on alignment algorithm optimization. We tested our evaluation methods on a trauma resuscitation dataset and provided the medical explanation of the activities and patterns identified as deviations using our proposed evaluation methods.
Multimodal Content Representation and Similarity Ranking of Movies
Venue: arXiv cs.IR 2017
Year: 2017

In this paper we examine the existence of correlation between movie similarity and low level features from respective movie content. In particular, we demonstrate the extraction of multi-modal representation models of movies based on subtitles, audio and metadata mining. We emphasize our research in topic modeling of movies based on their subtitles. In order to demonstrate the proposed content representation approach, we have built a small dataset of 160 widely known movies. We assert movie similarities, as propagated by the singular modalities and fusion models, in the form of recommendation rankings. We showcase a novel topic model browser for movies that allows for exploration of the different aspects of similarities between movies and an information retrieval system for movie similarity based on multi-modal content.
ns-3 Implementation of the 3GPP MIMO Channel Model for Frequency Spectrum above 6 GHz
Venue: arXiv cs.IT 2017
Year: 2017

Communications at mmWave frequencies will be a key enabler of the next generation of cellular networks, due to the multi-Gbps rate that can be achieved. However, there are still several problems that must be solved before this technology can be widely adopted, primarily associated with the interplay between the variability of mmWave links and the complexity of mobile networks. An end-to-end network simulator represents a great tool to assess the performance of any proposed solution to meet the stringent 5G requirements. Given the criticality of channel propagation characteristics at higher frequencies, we present our implementation of the 3GPP channel model for the 6-100 GHz band for the ns-3 end-to-end 5G mmWave module, and detail its associated MIMO beamforming architecture.
Load Synchronization and Sustained Oscillations Induced by Transactive Control
Venue: arXiv cs.SY 2017
Year: 2017

Transactive or market-based coordination strategies have recently been proposed for controlling the aggregate demand of a large number of electric loads. Such schemes offer operational benefits such as enforcing distribution feeder capacity limits and providing users with flexibility to consume energy based on the price they are willing to pay. However, this paper demonstrates that they are also prone to load synchronization and power oscillations. A transactive energy framework has been adopted and applied to a population of thermostatically controlled loads (TCLs). A modified TCL switching logic takes into account market coordination signals, alongside the natural hysteresis-based switching conditions. Studies of this market-based coordination scheme suggest that several factors may contribute to load synchronism, including sharp changes in the market prices that are broadcast to loads, lack of diversity in user specified bid curves, low feeder limits that are encountered periodically, and the form of user bid curves. Case studies illustrate challenges associated with market-based coordination strategies and provide insights into modifications that address those issues.
Generalizing Jensen and Bregman divergences with comparative convexity and the statistical Bhattacharyya distances with comparable means
Venue: arXiv cs.IT 2017
Year: 2017

Comparative convexity is a generalization of convexity relying on abstract notions of means. We define the Jensen divergence and the Jensen diversity from the viewpoint of comparative convexity, and show how to obtain the generalized Bregman divergences as limit cases of skewed Jensen divergences. In particular, we report explicit formula of these generalized Bregman divergences when considering quasi-arithmetic means. Finally, we introduce a generalization of the Bhattacharyya statistical distances based on comparative means using relative convexity.
Fast and unsupervised methods for multilingual cognate clustering
Venue: arXiv cs.CL 2017
Year: 2017

In this paper we explore the use of unsupervised methods for detecting cognates in multilingual word lists. We use online EM to train sound segment similarity weights for computing similarity between two words. We tested our online systems on geographically spread sixteen different language groups of the world and show that the Online PMI system (Pointwise Mutual Information) outperforms a HMM based system and two linguistically motivated systems: LexStat and ALINE. Our results suggest that a PMI system trained in an online fashion can be used by historical linguists for fast and accurate identification of cognates in not so well-studied language families.
Station-keeping control of an unmanned surface vehicle exposed to current and wind disturbances
Venue: arXiv cs.SY 2017
Year: 2017

Field trials of a 4 meter long, 180 kilogram, unmanned surface vehicle (USV) have been conducted to evaluate the performance of station-keeping heading and position controllers in an outdoor marine environment disturbed by wind and current. The USV has a twin hull configuration and a custom-designed propulsion system, which consists of two azimuthing thrusters, one for each hull. Nonlinear proportional derivative, backstepping and sliding mode feedback controllers were tested in winds of about 4-5 knots, with and without wind feedforward control. The controllers were tested when the longitudinal axis of the USV was aligned with the mean wind direction and when the longitudinal axis was perpendicular to the mean wind direction. It was found that the sliding mode controller performed best overall and that the addition of wind feedforward control did not significantly improve its effectiveness. However, wind feedforward control did substantially improve the performance of the proportional derivative and backstepping controllers when the mean wind direction was perpendicular to the longitudinal axis of the USV. An analysis of the length scales present in the power spectrum of the turbulent speed fluctuations in the wind suggests that a single anemometer is sufficient to characterize the speed and direction of the wind acting on the USV.
Reflexive Regular Equivalence for Bipartite Data
Venue: arXiv cs.LG 2017
Year: 2017

Bipartite data is common in data engineering and brings unique challenges, particularly when it comes to clustering tasks that impose on strong structural assumptions. This work presents an unsupervised method for assessing similarity in bipartite data. Similar to some co-clustering methods, the method is based on regular equivalence in graphs. The algorithm uses spectral properties of a bipartite adjacency matrix to estimate similarity in both dimensions. The method is reflexive in that similarity in one dimension is used to inform similarity in the other. Reflexive regular equivalence can also use the structure of transitivities -- in a network sense -- the contribution of which is controlled by the algorithm's only free-parameter, $α$. The method is completely unsupervised and can be used to validate assumptions of co-similarity, which are required but often untested, in co-clustering analyses. Three variants of the method with different normalizations are tested on synthetic data. The method is found to be robust to noise and well-suited to asymmetric co-similar structure, making it particularly informative for cluster analysis and recommendation in bipartite data of unknown structure. In experiments, the convergence and speed of the algorithm are found to be stable for different levels of noise. Real-world data from a network of malaria genes are analyzed, where the similarity produced by the reflexive method is shown to out-perform other measures' ability to correctly classify genes.
Existential Assertions for Voting Protocols
Venue: arXiv cs.CR 2017
Year: 2017

In earlier work, we extend the Dolev-Yao model with assertions. We build on that work and add existential abstraction to the language, which allows us to translate common constructs used in voting protocols into proof properties. We also give an equivalence-based definition of anonymity in this model, and prove anonymity for the FOO voting protocol.
Mining Behavioral Patterns from Millions of Android Users
Venue: arXiv cs.CY 2017
Year: 2017

The prevalence of smart mobile devices has promoted the popularity of mobile applications (a.k.a. apps). Supporting mobility has become a promising trend in software engineering research. This article presents an empirical study of behavioral service profiles collected from millions of users whose devices are deployed with Wandoujia, a leading Android app store service in China. The dataset of Wandoujia service profiles consists of two kinds of user behavioral data from using 0.28 million free Android apps, including (1) app management activities (i.e., downloading, updating, and uninstalling apps) from over 17 million unique users and (2) app network usage from over 6 million unique users. We explore multiple aspects of such behavioral data and present patterns of app usage. Based on the findings as well as derived knowledge, we also suggest some new open opportunities and challenges that can be explored by the research community, including app development, deployment, delivery, revenue, etc.
Improving Text Proposals for Scene Images with Fully Convolutional Networks
Venue: arXiv cs.CV 2017
Year: 2017

Text Proposals have emerged as a class-dependent version of object proposals - efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text recognition. In this paper we propose an improvement over the original Text Proposals algorithm of Gomez and Karatzas (2016), combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art.
Automatic Handgun Detection Alarm in Videos Using Deep Learning
Venue: arXiv cs.CV 2017
Year: 2017

Current surveillance and control systems still require human supervision and intervention. This work presents a novel automatic handgun detection system in videos appropriate for both, surveillance and control purposes. We reformulate this detection problem into the problem of minimizing false positives and solve it by building the key training data-set guided by the results of a deep Convolutional Neural Networks (CNN) classifier, then assessing the best classification model under two approaches, the sliding window approach and region proposal approach. The most promising results are obtained by Faster R-CNN based model trained on our new database. The best detector show a high potential even in low quality youtube videos and provides satisfactory results as automatic alarm system. Among 30 scenes, it successfully activates the alarm after five successive true positives in less than 0.2 seconds, in 27 scenes. We also define a new metric, Alarm Activation per Interval (AApI), to assess the performance of a detection model as an automatic detection system in videos.
An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models
Venue: arXiv cs.CV 2017
Year: 2017

Motion detection in video is important for a number of applications and fields. In video surveillance, motion detection is an essential accompaniment to activity recognition for early warning systems. Robotics also has much to gain from motion detection and segmentation, particularly in high speed motion tracking for tactile systems. There are a myriad of techniques for detecting and masking motion in an image. Successful systems have used Gaussian Models to discern background from foreground in an image (motion from static imagery). However, particularly in the case of a moving camera or frame of reference, it is necessary to compensate for the motion of the camera when attempting to discern objects moving in the foreground. For example, it is possible to estimate motion of the camera through optical flow methods or temporal differencing and then compensate for this motion in a background subtraction model. We selection a method by Yi et al. using Dual-Mode Single Gaussian Models which does just this. We implement the technique in Intel's Thread Building Blocks (TBB) and NVIDIA's CUDA libraries. We then compare parallelization improvements with a theoretical analysis of speedups based on the characteristics of our selected model and attributes of both TBB and CUDA. We make our implementation available to the public.
Set-Membership Information Fusion for Multisensor Nonlinear Dynamic Systems
Venue: arXiv cs.IT 2017
Year: 2017

The set-membership information fusion problem is investigated for general multisensor nonlinear dynamic systems. Compared with linear dynamic systems and point estimation fusion in mean squared error sense, it is a more challenging nonconvex optimization problem. Usually, to solve this problem, people try to find an efficient or heuristic fusion algorithm. It is no doubt that an analytical fusion formula should be much significant for rasing accuracy and reducing computational burden. However, since it is a more complicated than the convex quadratic optimization problem for linear point estimation fusion, it is not easy to get the analytical fusion formula. In order to overcome the difficulty of this problem, two popular fusion architectures are considered: centralized and distributed set-membership information fusion. Firstly, both of them can be converted into a semidefinite programming problem which can be efficiently computed, respectively. Secondly, their analytical solutions can be derived surprisingly by using decoupling technique. It is very interesting that they are quite similar in form to the classic information filter. In the two analytical fusion formulae, the information of each sensor can be clearly characterized, and the knowledge of the correlation among measurement noises across sensors are not required. Finally, multi-algorithm fusion is used to minimize the size of the state bounding ellipsoid by complementary advantages of multiple parallel algorithms. A typical numerical example in target tracking demonstrates the effectiveness of the centralized, distributed, and multi-algorithm set-membership fusion algorithms. In particular, it shows that multi-algorithm fusion performs better than the centralized and distributed fusion.
An Information-Theoretic Analysis of the Gaussian Multicast Channel with Interactive User Cooperation
Venue: arXiv cs.IT 2017
Year: 2017

We consider the transmission of a common message from a transmitter to three receivers over a broadcast channel, referred to as a multicast channel in this case. All the receivers are allowed to cooperate with each other over full-duplex bi-directional non-orthogonal cooperation links. We investigate the information-theoretic upper and lower bounds on the transmission rate. In particular, we propose a three-receiver fully interactive cooperation scheme (3FC) based on superpositions of CF and DF at the receivers. In the 3FC scheme, the receivers interactively perform compress-forward (CF) simultaneously to initiate the scheme, and then decode-forward (DF) sequentially to allow a correlation of each layer of the DF superposition in cooperation with the transmitter toward the next receiver in the chain to improve the achievable rate. The analysis leads to a closed-form expression that allows for numerical evaluation, and also gives some insight on key points to design interactive schemes. The numerical results provided in the Gaussian case show that the proposed scheme outperforms existing schemes and show the benefit of interaction.
LHCb trigger streams optimization
Venue: arXiv cs.DC 2017
Year: 2017

The LHCb experiment stores around $10^{11}$ collision events per year. A typical physics analysis deals with a final sample of up to $10^7$ events. Event preselection algorithms (lines) are used for data reduction. Since the data are stored in a format that requires sequential access, the lines are grouped into several output file streams, in order to increase the efficiency of user analysis jobs that read these data. The scheme efficiency heavily depends on the stream composition. By putting similar lines together and balancing the stream sizes it is possible to reduce the overhead. We present a method for finding an optimal stream composition. The method is applied to a part of the LHCb data (Turbo stream) on the stage where it is prepared for user physics analysis. This results in an expected improvement of 15% in the speed of user analysis jobs, and will be applied on data to be recorded in 2017.
Control of an Unmanned Surface Vehicle with Uncertain Displacement and Drag
Venue: arXiv cs.SY 2017
Year: 2017

Experimental testing of an unmanned surface vehicle (USV) has been performed to evaluate the performance of two low-level controllers when displacement and drag properties are time-varying and uncertain. The USV is a 4.3 meter long, 150 kilogram wave adaptive modular vessel (WAM-V) with an inflatable twin hull configuration and waterjet propulsion. Open loop maneuvering tests were conducted to characterize the dynamics of the vehicle. The hydrodynamic coefficients of the vehicle were determined through system identification of the maneuvering data and were used for simulations during control system development. The resulting controllers were experimentally field tested on-water. Variable mass and drag tests show that the vehicle is best controlled by a model reference adaptive backstepping speed and heading controller. The backstepping controller developed by Liao et. al (2010) is modified to account for an overprediction of necessary control action and motor saturation. It is shown that when an adaptive algorithm is implemented for the surge control subsystem of the modified backstepping controller, the effects of variable mass and drag are mitigated.
Algorithms for Online Influencer Marketing
Venue: arXiv cs.SI 2017
Year: 2017

Influence maximization is the problem of finding influential users, or nodes, in a graph so as to maximize the spread of information. It has many applications in advertising and marketing on social networks. In this paper, we study a highly generic version of influence maximization, one of optimizing influence campaigns by sequentially selecting "spread seeds" from a set of influencers, a small subset of the node population, under the hypothesis that, in a given campaign, previously activated nodes remain "persistently" active throughout and thus do not yield further rewards. This problem is in particular relevant for an important form of online marketing, known as influencer marketing, in which the marketers target a sub-population of influential people, instead of the entire base of potential buyers. Importantly, we make no assumptions on the underlying diffusion model and we work in a setting where neither a diffusion network nor historical activation data are available. We call this problem online influencer marketing with persistence (in short, OIMP). We first discuss motivating scenarios and present our general approach. We introduce an estimator on the influencers' remaining potential -- the expected number of nodes that can still be reached from a given influencer -- and justify its strength to rapidly estimate the desired value, relying on real data gathered from Twitter. We then describe a novel algorithm, GT-UCB, relying on upper confidence bounds on the remaining potential. We show that our approach leads to high-quality spreads on both simulated and real datasets, even though it makes almost no assumptions on the diffusion medium. Importantly, it is orders of magnitude faster than state-of-the-art influence maximization methods, making it possible to deal with large-scale online scenarios.
Computational topology of graphs on surfaces
Venue: arXiv cs.CG 2017
Year: 2017

Computational topology is an area that revisits topological problems from an algorithmic point of view, and develops topological tools for improved algorithms. We survey results in computational topology that are concerned with graphs drawn on surfaces. Typical questions include representing surfaces and graphs embedded on them computationally, deciding whether a graph embeds on a surface, solving computational problems related to homotopy, optimizing curves and graphs on surfaces, and solving standard graph algorithm problems more efficiently in the case of surface-embedded graphs.
3D Cell Nuclei Segmentation with Balanced Graph Partitioning
Venue: arXiv cs.CV 2017
Year: 2017

Cell nuclei segmentation is one of the most important tasks in the analysis of biomedical images. With ever-growing sizes and amounts of three-dimensional images to be processed, there is a need for better and faster segmentation methods. Graph-based image segmentation has seen a rise in popularity in recent years, but is seen as very costly with regard to computational demand. We propose a new segmentation algorithm which overcomes these limitations. Our method uses recursive balanced graph partitioning to segment foreground components of a fast and efficient binarization. We construct a model for the cell nuclei to guide the partitioning process. Our algorithm is compared to other state-of-the-art segmentation algorithms in an experimental evaluation on two sets of realistically simulated inputs. Our method is faster, has similar or better quality and an acceptable memory overhead.
Java Code Analysis and Transformation into AWS Lambda Functions
Venue: arXiv cs.DC 2017
Year: 2017

Software developers are faced with the issue of either adapting their programming model to the execution model (e.g. cloud platforms) or finding appropriate tools to adapt the model and code automatically. A recent execution model which would benefit from automated enablement is Function-as-a-Service. Automating this process requires a pipeline which includes steps for code analysis, transformation and deployment. In this paper, we outline the design and runtime characteristics of Podilizer, a tool which implements the pipeline specifically for Java source code as input and AWS Lambda as output. We contribute technical and economic metrics about this concrete 'FaaSification' process by observing the behaviour of Podilizer with two representative Java software projects.